Affiliation:
1. Escuela Politécnica Nacional, Facultad de Ingeniería de Sistemas, Quito, Ecuador
Abstract
One of the primary processes in tax administration is debt collection management. The objective of this process, among others, is to recover economic resources that have been declared by taxpayers. Due to limitations in tax administration such as staffing, tools, time, and others, tax administrations seek to recover debts in the early stages of control where collection costs are lower than in subsequent stages. To optimize the debt collection management process and contribute to decision-making, this study proposes a deep learning-based framework to detect atypical behaviors of taxpayers with a high probability of non-payment. Normal and atypical behavior groups were also analyzed to identify interesting events using association rules.
Publisher
Escuela Politecnica Nacional
Subject
Applied Mathematics,Geochemistry and Petrology,Physics and Astronomy (miscellaneous),General Engineering,Geotechnical Engineering and Engineering Geology,Environmental Engineering,Chemistry (miscellaneous)
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